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Record W2039210878 · doi:10.1139/x07-030

Differential responsiveness in stem height and diameter growth of two<i>Ficus</i>tree species in the Three Gorges reservoir region of China as affected by branch removal

2007· article· en· W2039210878 on OpenAlexvenueno aff
Xiao-Ping Zhang, Bo Zeng, Zhong Zhangcheng

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
FundersSouthwest University
KeywordsFicusCuttingBiologyMain stemBotanyBasal areaHorticultureCrown (dentistry)MoraceaeEcology

Abstract

fetched live from OpenAlex

In the Three Gorges reservoir region of China, Ficus microcarpa L. and Ficus virens Ait. var. sublanceolata (Miq.) Corner (Moraceae) are widely used in greening and ecological restoration following construction, including roads, railways, towns, etc. Branch cuttings are used for cultivating saplings of these trees. We conducted a 4 year experiment that included four branch-removal intensities to evaluate the influence of branch removal on stem height and diameter growth of these Ficus spp. It was found that branch removal did not affect the stem height growth of either F. microcarpa or F. virens, but decreased the growth of their stem basal diameters. The reduction in growth of stem basal diameter was intensified with branch removal. As expected, branch removal decreased the tapering of whole tree stems, but this effect was mainly due to the alteration of the shape of the bare stem part, and the shape of the stem part within the intact upper crown was not affected by the treatment. The data clearly showed that stem height growth was less sensitive than stem diameter growth to branch removal, and that the response of stem diameter growth to branch removal differed between bare and intact stem parts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.273
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2007
Admission routes1
Has abstractyes

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